Developing Marketing Personas with Machine Learning for Educational Program Finder

Abstract

The motivation for the work is to see if marketing personas can be created with an educational Program Finder using machine learning. The research questions for the master’s thesis are “By using machine learning to process user behaviour, will the marketing personas improve in quality?” and “Can marketing and sales benefit from machine learning made personas?”. With the first research question, the thesis uses existing marketing personas created by Aalto University Executive Education and references them with the marketing personas created with machine learning. The second research question is answered by conducting three end-user interviews. The end-users all had marketing and sales working context and were chosen from Aalto University Executive Education. The approach for the thesis is to create a hypothesis of machine learning algorithms that could create marketing personas. The machine learning framework chosen for the thesis is semi-structured that implements labelled clusters to which build the user behaviour to. User behaviour is collected from users interacting with the filters of an educational Program Finder. The thesis introduces a marketing persona, Generic Marketing Persona and for a deeper analysis, the Data Behind the Persona. The Generic Marketing Persona uses the machine learning algorithms and is created from the labelled clusters. The Generic Marketing Persona has a template for which to build on and uses the cluster data to enrich the template with the data. The Data Behind the Persona is a presentation of charts that are extracted from the cluster data. The results for the thesis are that the machine learning personas increased the quality when referenced to the existing ones. The machine learning personas were more detailed, based on data and communicated the needs of the target groups more efficiently. However, the Generic Marketing Persona was proven to be unusable for taking marketing and sales actions because the information was too generic. Interviewees though found many possible use cases for the Data Behind the Persona, including content producing, target group revision, lead valuing and market trend analysis.